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arXiv 2307.04539cond-mat.softcond-mat.stat-mech
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非均匀流体的神经泛函理论:基础与应用

Neural functional theory for inhomogeneous fluids: Fundamentals and applications

Florian Sammüller, Sophie Hermann, Daniel de las Heras, Matthias Schmidt

AI总结:

本文结合经典密度泛函理论与深度神经网络学习硬球流体的精确单体直接关联泛函,利用自动微分和泛函线积分获得高阶关联与自由能,并以低成本自洽预测宏观密度分布,精度优于基本测量理论。

摘要:

我们提出了一种基于经典密度泛函理论和机器学习的混合方案,用于确定非均匀流体的平衡结构与热力学性质。从密度分布到单体直接关联函数的精确泛函映射由深度神经网络在局部表示。我们针对硬球流体论证了该通用框架,并在训练及参考中使用随机化外部环境下系统的巨正则蒙特卡洛模拟数据。泛函演算在神经网络基础上实现,以通过自动微分获取高阶关联函数,并通过泛函线积分获取自由能。热 Noether 和规则得到显式验证。我们展示了神经泛函在密度分布自洽计算中的应用。其结果优于最先进的基本测量密度泛函理论。求解相关 Euler-Lagrange 方程的低成本使得能够弥合原始训练数据的系统尺寸与宏观预测之间的差距,同时保持接近模拟的微观精度。这些结果确立了泛函的机器学习作为软物质多尺度描述中的有效工具。

英文摘要:

We present a hybrid scheme based on classical density functional theory and machine learning for determining the equilibrium structure and thermodynamics of inhomogeneous fluids. The exact functional map from the density profile to the one-body direct correlation function is represented locally by a deep neural network. We substantiate the general framework for the hard sphere fluid and use grand canonical Monte Carlo simulation data of systems in randomized external environments during training and as reference. Functional calculus is implemented on the basis of the neural network to access higher-order correlation functions via automatic differentiation and the free energy via functional line integration. Thermal Noether sum rules are validated explicitly. We demonstrate the use of the neural functional in the self-consistent calculation of density profiles. The results outperform those from state-of-the-art fundamental measure density functional theory. The low cost of solving an associated Euler-Lagrange equation allows to bridge the gap from the system size of the original training data to macroscopic predictions upon maintaining near-simulation microscopic precision. These results establish the machine learning of functionals as an effective tool in the multiscale description of soft matter.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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